Triple

T5110750
Position Surface form Disambiguated ID Type / Status
Subject London Film Festival E115206 entity
Predicate hasSection P35 FINISHED
Object Galas
Galas is a prestigious showcase section of the London Film Festival featuring high-profile premieres and red-carpet screenings.
 E493879 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Galas | Statement: [London Film Festival, hasSection, Galas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Galas
Context triple: [London Film Festival, hasSection, Galas]
  • A. Gala
    Gala was the Russian-born muse, model, and wife of surrealist painter Salvador Dalí, renowned for her profound influence on his life and work.
  • B. Gauthier
    Gauthier is a French given name and surname, equivalent to the English name Walter and historically borne by various notable figures in France and other Francophone regions.
  • C. Langella
    Langella is an Italian-origin surname most notably borne by acclaimed American actor Frank Langella.
  • D. Cecilia
    Cecilia is a feminine given name of Latin origin, traditionally associated with Saint Cecilia, the patron saint of music.
  • E. La Goulue
    La Goulue was the stage name of Louise Weber, a famous late-19th-century French can-can dancer at the Moulin Rouge and a popular subject of Toulouse-Lautrec’s posters.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Galas
Triple: [London Film Festival, hasSection, Galas]
Generated description
Galas is a prestigious showcase section of the London Film Festival featuring high-profile premieres and red-carpet screenings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Galas
Target entity description: Galas is a prestigious showcase section of the London Film Festival featuring high-profile premieres and red-carpet screenings.
  • A. Gala
    Gala was the Russian-born muse, model, and wife of surrealist painter Salvador Dalí, renowned for her profound influence on his life and work.
  • B. Gauthier
    Gauthier is a French given name and surname, equivalent to the English name Walter and historically borne by various notable figures in France and other Francophone regions.
  • C. Langella
    Langella is an Italian-origin surname most notably borne by acclaimed American actor Frank Langella.
  • D. Cecilia
    Cecilia is a feminine given name of Latin origin, traditionally associated with Saint Cecilia, the patron saint of music.
  • E. La Goulue
    La Goulue was the stage name of Louise Weber, a famous late-19th-century French can-can dancer at the Moulin Rouge and a popular subject of Toulouse-Lautrec’s posters.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69bd4441d1648190a54a533895041987 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd75ad362c8190b9cbded390aaea3c completed March 20, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bebaa719748190930dceaeedb346c2 completed March 21, 2026, 3:35 p.m.
NEDg Description generation batch_69bebb8e5f1c819082f0b59e9524d6e8 completed March 21, 2026, 3:38 p.m.
NED2 Entity disambiguation (via description) batch_69bebc30029081909b5c937308fefcf2 completed March 21, 2026, 3:41 p.m.
Created at: March 20, 2026, 1:41 p.m.